



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	BarthezTokenizer
			A      		=	BarthezTokenizerFast
			A      		=	True
			A      		=	True

			def      __snake_case       (self					)       ->   List[Any]:
						super().setUp()

						UpperCAmelCase_:		Dict							      =     BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""					)
						tokenizer.save_pretrained(self.tmpdirname					)
						tokenizer.save_pretrained(self.tmpdirname,   legacy_format=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     tokenizer

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		List[str]							      =     """<pad>"""
						UpperCAmelCase_:		Union[str, Any]							      =     1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_					)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		List[Any]							      =     list(self.get_tokenizer().get_vocab().keys()					)

						self.assertEqual(vocab_keys[0],   """<s>"""					)
						self.assertEqual(vocab_keys[1],   """<pad>"""					)
						self.assertEqual(vocab_keys[-1],   """<mask>"""					)
						self.assertEqual(len(SCREAMING_SNAKE_CASE_					),   101122					)

			def      __snake_case       (self					)       ->   int:
						self.assertEqual(self.get_tokenizer().vocab_size,   101122					)

			@require_torch
			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Any							      =     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
						UpperCAmelCase_:		str							      =     [0, 57, 3018, 70307, 91, 2]

						UpperCAmelCase_:		Optional[int]							      =     self.tokenizer(
						    SCREAMING_SNAKE_CASE_,   max_length=len(SCREAMING_SNAKE_CASE_					),   padding=SCREAMING_SNAKE_CASE_,   truncation=SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					)
						self.assertIsInstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						self.assertEqual((2, 6),   batch.input_ids.shape					)
						self.assertEqual((2, 6),   batch.attention_mask.shape					)
						UpperCAmelCase_:		Dict							      =     batch.input_ids.tolist()[0]
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						if not self.test_rust_tokenizer:
									return

						UpperCAmelCase_:		Optional[Any]							      =     self.get_tokenizer()
						UpperCAmelCase_:		int							      =     self.get_rust_tokenizer()

						UpperCAmelCase_:		Optional[Any]							      =     """I was born in 92000, and this is falsé."""

						UpperCAmelCase_:		List[str]							      =     tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[Any]							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     self.get_rust_tokenizer()
						UpperCAmelCase_:		List[Any]							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_					)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			@slow
			def      __snake_case       (self					)       ->   Any:
						# fmt: off
						UpperCAmelCase_:		Optional[Any]							      =     {"""input_ids""": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# moussaKam/mbarthez is a french model. So we also use french texts.
						UpperCAmelCase_:		str							      =     [
						    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
						    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
						    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
						    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
						    """telles que la traduction et la synthèse de texte.""",
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=SCREAMING_SNAKE_CASE_,   model_name="""moussaKam/mbarthez""",   revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6""",   sequences=SCREAMING_SNAKE_CASE_,   )











import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a      :  int			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     """huggingface/label-files"""
			UpperCAmelCase_:		Optional[int]							      =     """imagenet-1k-id2label.json"""
			UpperCAmelCase_:		Union[str, Any]							      =     json.load(open(hf_hub_download(lowerCAmelCase__		,						lowerCAmelCase__		,						repo_type="""dataset"""  )		,						"""r"""  )  )
			UpperCAmelCase_:		Optional[Any]							      =     {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
			UpperCAmelCase_:		Tuple							      =     {v: k for k, v in idalabel.items()}

			UpperCAmelCase_:		int							      =     """std_conv""" if """bit""" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			UpperCAmelCase_:		Any							      =     BitConfig(
			    conv_layer=lowerCAmelCase__		,						num_labels=1_0_0_0		,						idalabel=lowerCAmelCase__		,						labelaid=lowerCAmelCase__		,						)

			return config







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]  ):





			"""simple docstring"""


			if "stem.conv" in name:
						UpperCAmelCase_:		Dict							      =     name.replace("""stem.conv"""		,						"""bit.embedder.convolution"""  )
			if "blocks" in name:
						UpperCAmelCase_:		Tuple							      =     name.replace("""blocks"""		,						"""layers"""  )
			if "head.fc" in name:
						UpperCAmelCase_:		int							      =     name.replace("""head.fc"""		,						"""classifier.1"""  )
			if name.startswith("""norm"""  ):
						UpperCAmelCase_:		List[Any]							      =     """bit.""" + name
			if "bit" not in name and "classifier" not in name:
						UpperCAmelCase_:		str							      =     """bit.encoder.""" + name

			return name







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase_:		Optional[int]							      =     Image.open(requests.get(lowerCAmelCase__		,						stream=lowerCAmelCase__  ).raw  )
			return im







@torch.no_grad()
def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		str=False  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     get_config(lowerCAmelCase__  )

			# load original model from timm
			UpperCAmelCase_:		str							      =     create_model(lowerCAmelCase__		,						pretrained=lowerCAmelCase__  )
			timm_model.eval()

			# load state_dict of original model
			UpperCAmelCase_:		Optional[Any]							      =     timm_model.state_dict()
			for key in state_dict.copy().keys():
						UpperCAmelCase_:		Any							      =     state_dict.pop(lowerCAmelCase__  )
						UpperCAmelCase_:		Optional[Any]							      =     val.squeeze() if """head""" in key else val

			# load HuggingFace model
			UpperCAmelCase_:		Union[str, Any]							      =     BitForImageClassification(lowerCAmelCase__  )
			model.eval()
			model.load_state_dict(lowerCAmelCase__  )

			# create image processor
			UpperCAmelCase_:		Any							      =     create_transform(**resolve_data_config({}		,						model=lowerCAmelCase__  )  )
			UpperCAmelCase_:		List[Any]							      =     transform.transforms

			UpperCAmelCase_:		Tuple							      =     {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			UpperCAmelCase_:		int							      =     BitImageProcessor(
			    do_resize=lowerCAmelCase__		,						size={"""shortest_edge""": timm_transforms[0].size}		,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,						do_center_crop=lowerCAmelCase__		,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}		,						do_normalize=lowerCAmelCase__		,						image_mean=timm_transforms[-1].mean.tolist()		,						image_std=timm_transforms[-1].std.tolist()		,						)

			UpperCAmelCase_:		Optional[Any]							      =     prepare_img()
			UpperCAmelCase_:		Tuple							      =     transform(lowerCAmelCase__  ).unsqueeze(0  )
			UpperCAmelCase_:		Dict							      =     processor(lowerCAmelCase__		,						return_tensors="""pt"""  ).pixel_values

			# verify pixel values
			assert torch.allclose(lowerCAmelCase__		,						lowerCAmelCase__  )

			# verify logits
			with torch.no_grad():
						UpperCAmelCase_:		Dict							      =     model(lowerCAmelCase__  )
						UpperCAmelCase_:		str							      =     outputs.logits

			print("""Logits:"""		,						logits[0, :3]  )
			print("""Predicted class:"""		,						model.config.idalabel[logits.argmax(-1  ).item()]  )
			UpperCAmelCase_:		List[str]							      =     timm_model(lowerCAmelCase__  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(lowerCAmelCase__		,						outputs.logits		,						atol=1e-3  )
			print("""Looks ok!"""  )

			if pytorch_dump_folder_path is not None:
						Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
						print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'  )
						model.save_pretrained(lowerCAmelCase__  )
						processor.save_pretrained(lowerCAmelCase__  )

			if push_to_hub:
						print(F'Pushing model {model_name} and processor to the hub'  )
						model.push_to_hub(F'ybelkada/{model_name}'  )
						processor.push_to_hub(F'ybelkada/{model_name}'  )


if __name__ == "__main__":
	a      :  Dict			     =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='resnetv2_50x1_bitm',
	    type=str,
	    help='Name of the BiT timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether to push the model to the hub.',
	)

	a      :  Tuple			     =			parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






